A Comparative Analysis of School Dropout Causes in Rural and Urban Romania

mihaela miron; larisa mistrean

Outline

Open Access

Research article

A Comparative Analysis of School Dropout Causes in Rural and Urban Romania

mihaela miron¹^*

,

larisa mistrean²

¹

Doctoral School of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700506 Iași, Romania

²

Behavioral Research Center, Academy of Economic Studies of Moldova, MD-2005 Chisinau, Republic of Moldova

Education Science and Management

|

Volume 2, Issue 3, 2024

|

Pages 134-144

https://doi.org/10.56578/esm020302

Received: 06-09-2024,

Revised: 08-18-2024,

Accepted: 08-24-2024,

Available online: 09-19-2024

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Abstract:

This study investigates the differences in the factors contributing to school dropout between rural and urban educational institutions in Romania, focusing on individual, family, school, and community dimensions. A sample of 557 participants, including educational directors, teachers, and administrators, was surveyed to assess the prevalence of various dropout causes. The Mann-Whitney U test was employed to identify statistically significant differences between rural and urban schools in specific dropout factors. The findings indicate that urban schools report higher incidences of individual-level issues such as substance abuse, juvenile delinquency, teenage pregnancy, and health-related problems. At the family level, urban institutions were more likely to encounter students with incarcerated parents or those placed in alternative care. School-related factors also varied, with urban schools being characterised by larger class sizes and insufficient access to counselling and guidance services, while rural schools were more affected by early school start times. In the community dimension, urban schools faced greater challenges with negative peer influences and a lack of educational facilities near students’ homes. These results suggest that the causes of dropout in urban settings are more complex, necessitating tailored interventions and resources. It is recommended that context-specific strategies be developed to address the distinct dropout factors in both rural and urban environments, thereby supporting more inclusive and effective educational policies in Romania.

Keywords: School dropout, Dropout causes, Rural-urban comparison, Educational disparities, Dropout prevention

1. Introduction

The school dropout phenomenon is defined as the premature interruption of education. This phenomenon has complex causes with a negative socio-economic impact on the individuals, which creates a vicious cycle where the lack of qualification and professional experience can lead to unemployment or precarious employment, generating a feeling of failure and disillusionment. Individuals who leave school prematurely are deprived of the opportunity to gain the knowledge and skills necessary to achieve their goals. Understanding the consequences of dropping out of school can motivate those people to reconsider their decision and return to school or pursue other forms of education and training, thereby improving their chances of getting a better job and a decent life. Investment in education and implementation of social support programs can prevent and combat school dropout, contributing to building a brighter and more prosperous future for the entire generation.

Data on school dropout and early school leaving in Romania indicate significant fluctuations between 2019 and 2023. According to the National Institute of Statistics, early school leaving in secondary education had a decreasing trend from 18.2% in 2019 to 16.5% in 2023. However, the COVID-19 pandemic had a considerable impact on the educational environment, leading to school closures and the transition to online education, which accentuated existing disparities and increased the risk of school dropout, especially among vulnerable students, in 2020 (Institutul Național de Statistică, 2023). This increase was partially mitigated by adaptive educational measures implemented in the following years.

Contributing factors at the national level include socio-economic, cultural, and educational factors. Low family income and limited access to educational means are among the main factors contributing to school dropout. In addition, internal and external labour migration affects children’s educational stability, with many having to interrupt their studies to move with their parents or enter the labour market. Regional differences, especially between urban and rural areas, affect access to quality education. According to the study by Ministerul Educației (2023), the lack of educational infrastructure and resources in rural areas limits learning opportunities. Cultural differences and the values of diverse communities can influence attitudes towards education. For example, in some rural or Roma communities, formal education is not always valued in the same way as in other settings, which can contribute to higher drop-out rates. The quality of education and insufficient school infrastructure are also critical factors. School curricula that are not adapted to the needs of the labour market or the interests of pupils, the lack of qualified teachers and adequate teaching resources, together with the physical conditions of schools, can discourage regular school attendance and limit the pupils’ educational success.

The causes that lead to school dropout are complex, which are grouped according to the literature on several distinct domains: individual, school, family and community. Specific causes for each of the domains are as follows, alongside their supportive literature:

a) Causes at the individual level:

• Reduced expectations regarding the educational environment (Fan & Wolters, 2012);

• Motivation deficit (Yusof et al., 2023);

• Poor health (Sabates et al., 2010);

• Low level of self-esteem (Pedditzi, 2024);

• Reduced involvement in extracurricular activities (O’Donnell et al., 2023); and educational deficiencies (Mireles-Rios et al., 2020);

• Repetition (Van der Berg et al., 2020);

• Drug and alcohol use (Stoddard et al., 2020);

• Special educational requirements (Cooc, 2023); and juvenile delinquency (Musa & Rais, 2023);

• Absenteeism (Rahman et al., 2023);

• Teenage pregnancies (Lestarini, 2023);

• Obesity (Diaz-Serrano & Stoyanova, 2023).

b) Causes generated by the school environment:

• The long commute (Zeragaber et al., 2024);

• Large number of students in class (Paulsrud & Nilholm, 2020);

• Limited resources at the school level (Agüero et al., 2021);

• Lack of empathy on the part of teachers (Meyers et al., 2019);

• Discrimination (Papachristou, 2023);

• Too restrictive school discipline (Bell & Puckett, 2020);

• Violence and aggressiveness (Ünlü & Avci, 2023);

• Non-existence of tablets/computers/mobile phones (Goldschmidt, 2020);

• Start time of school activities (Owens et al., 2010).

c) Causes stemming from the family environment:

• Poverty (De Witte et al., 2013);

• Parents leaving to work abroad (Silverstein & Zuo, 2021);

• Parental educational level (Marlow & Rehman, 2021);

• Exploitation of children in the household space (Mihigo et al., 2024);

• The family’s perception of the importance of education (Adelman et al., 2018);

• Early marriages (Ratusniak & Silva, 2023);

• Migrant and refugee families (Bove & Sharmahd, 2020);

• Disorganized families (Gordon & Nandy, 2012);

• Incarcerated parents (Del Toro et al., 2022);

• Families who sexually exploit children (Rafferty, 2016);

• Exploitation of children through begging activities (Delap, 2009);

• Caring for a relative (Dreier & Luce, 2023);

• Children in hunger (Kılıç, 2022);

• Domestic violence (Alifiyah & Anshori, 2023);

• Children in alternative care (Petrowski et al., 2017);

• Death of family members (Levkovich & Elyoseph, 2021).

d) Causes generated by the community influence:

• Entourage (Paraman & Hussain, 2022);

• The online environment (Muthami et al., 2023);

• Low job opportunities (Guío et al., 2016);

• Climate change and natural disasters (Haste & Chopra, 2020);

• COVID-19 pandemic (Haste & Chopra, 2020);

• Trafficked children (Albright et al., 2020);

• Online sexual exploitation (Dimitropoulos et al., 2022);

• Global recession (General Assembly Security Council, 2021).

The social implications of early school leaving are vast, affecting not only the individuals, but also the society as a whole. Students who leave the education system early are more likely to have difficulty integrating into the labor market and are more likely to enter cycles of poverty (Vadivel et al., 2023). The results of several studies confirm that initiatives that address the socio-economic needs of students, such as school meal programs (Bliss, 2024) and subsidies for teaching materials, contribute to reducing dropout. This is a reflection of the social capital model that suggests that community support and resources are essential for school retention. As such, it is needed to investigate the differences between rural and urban schools in order to develop a tailored approach to dropout prevention, with the aim of building resilient and inclusive education systems capable of responding effectively to the needs of all students.

2. Methodology

Data on participants in this study were collected from educational institutions in Romania. This study included respondents actively involved in the educational process, such as directors, teachers, or administrators. Out of 557 total respondents, 203 were currently working in rural schools (36.4%), whereas 354 were active in urban educational institutions (63.6%).

Respondents were asked to rate (from rarely to almost always) how often different aspects were the cause of school dropout in their institutions. Considering that all items were evaluated on a 6-point Likert scale, the statistical analysis for comparisons between urban and rural schools was performed using the nonparametric Mann-Whitney U test, with an established significance threshold of α=0.05. The magnitude of the effect of the independent variable on the tested items was evaluated on the basis of η², measuring the proportion of variance associated with the main effect of the independent variable.

3. Results

3.1 Individual Dimension

To evaluate the established hypotheses, a series of bivariate analyses were conducted between the respondents’ area of school residence and their responses to various aspects of school dropout. In regards to the causes pertaining to the individual dimension, 14 options were presented to respondents for evaluation. Statistically significant differences were not identified in ten of these causes, as outlined below:

• Early marriage (U=35507, p=0.808)

• Delayed school start of the student (U=33563, p=0.18)

• Special educational requirements (U=32777.5, p=0.078)

• School failure (U=35710.5, p=0.902)

• Lack of motivation to study (U=34647.5, p=0.473)

• Lack of trust in the educational system (U=34203.5, p=0.335)

• High rate of absenteeism (U=35094.5, p=0.641)

• Repetition (U=33765.5, p=0.229)

• Reduced involvement in extracurricular activities (U=35034.5, p=0.618)

• Decreased self-esteem (U=35703.5, p=0.899)

Table 1 shows the comparison between rural and urban schools in terms of the individual causes of school dropout using the Mann-Whitney test. The results of the non-parametric test for independent samples confirm statistically significant differences (U=31810.5, p=0.015) between urban (M=2.17, SD=1.423, Mdn=2) and rural schools (M=1.83, SD=1.157, Mdn=1) on the cause of drug/alcohol consumption. The magnitude of the effect of the independent variable is η²=0.0106, and at least 1.1% of the variance in rank can be explained by the area of school residence.

Table 1. Comparison between rural and urban schools in terms of the individual causes of school dropout

	Mann-Whitney U	Z	Asymp. Sig. (2-Tailed)	η²	Mean Rank for the Urban	Mean Rank for the Rural
Early marriage	35507	-0.244	0.808	0.000	280.2	276.91
Drug/alcohol use	31810.5	-2.435	0.015	0.011	290.64	258.7
Student’s delayed school start	33563	-1.34	0.18	0.003	285.69	267.33
Juvenile delinquency	31998.5	-2.211	0.027	0.009	290.11	259.63
Special educational requirements	32777.5	-1.763	0.078	0.006	287.91	263.47
School failure	35710.5	-0.123	0.902	0.000	279.62	277.91
Pregnancy	32134.5	-2.233	0.026	0.009	289.72	260.3
Lack of motivation to study	34647.5	-0.718	0.473	0.001	275.37	285.32
Lack of confidence in the educational system	34203.5	-0.963	0.335	0.002	283.88	270.49
Health problems	29557	-3.621	0	0.024	297.01	247.6
High rate of absenteeism	35094.5	-0.466	0.641	0.000	281.36	274.88
Grade repetition	33765.5	-1.202	0.229	0.003	272.88	289.67
Reduced involvement in extracurricular activities	35034.5	-0.499	0.618	0.000	276.47	283.42
Decreased self-esteem	35703.5	-0.126	0.899	0.000	279.64	277.88

According to the Mann-Whitney U test, this study confirms the existence of a statistically significant difference (U=31998.5, p=0.027) between schools in the urban environment (M=2.68, SD=1.474, Mdn=2) and those in the rural environment (M=2.38, SD =1,331, Mdn=2) regarding the juvenile delinquency case. The magnitude of the effect of the independent variable is η²=0.0087, and at least 0.9% of the variance in rank can be explained by the area of school residence.

Pregnancy is a cause, whose statistically significant differences (U=32134.5, p=0.026) between schools in the urban environment (M=2.17, SD=1.421, Mdn=2) and those in the rural environment (M=1.91, SD=1.281, Mdn=1) can be identified. The magnitude of the effect of the independent variable is also minimal with η²=0.0089, and at least 0.9% of the variance in rank can be explained by the area of school residence.

On the cause of health problems, the results of the non-parametric Mann-Whitney U test confirm a statistically significant difference (U=29557, p=0) between schools in the urban environment (M=2.47, SD=1.386, Mdn=2) and those in the rural environment (M=2.01, SD=1.115, Mdn=2). The magnitude of the effect of the independent variable is η²=0.0235, and at least 2.4% of the rank variance can be explained by the area of school residence, which is the largest effect observed at the level of the learner.

Figure 1 shows the mean ranks for the causes of school dropout at the individual level. The hypothesis positing significant differences between urban and rural schools in the prevalence of these individual-level causes was partially confirmed. Statistically significant differences were obtained for four out of 14 causes (drug/alcohol use, juvenile delinquency, pregnancy and health issues), with a higher incidence for urban schools.

Figure 1. Mean ranks for the causes of school dropout at the individual level

3.2 Family Dimension

Considering the student’s family situation, out of the nine causes presented to the respondents, no significant differences between the rural and urban population were identified in seven of them as follows:

• Exploitation in household work/younger siblings in care (U=34036.5, p=0.29)

• Lack of a stable domicile/frequent moves without legal forms (U=34919.5, p=0.571)

• School dropout registered in other family members (U=34840, p=0.544)

• The low level of family income (U=34579, p=0.451)

• Parents’ low level of education (U=34707.5, p=0.493)

• Parents leaving to work abroad (U=35885, p=0.98)

• Domestic violence in the family (U=34163, p=0.326)

Table 2. Comparison between rural and urban schools at the family causes of school dropout

	Mann-Whitney U	Z	Asymp. Sig. (2-Tailed)	η²	Mean Rank for the Urban	Mean Rank for the Rural
Placement in alternative care	29066.5	-3.863	0	0.027	298.39	245.18
Exploitation in household work/younger siblings in care	34036.5	-1.057	0.29	0.002	273.65	288.33
Incarcerated parents	31150	-2.835	0.005	0.014	292.51	255.45
Lack of a stable domicile/frequent moves without legal forms	34919.5	-0.567	0.571	0.001	281.86	274.02
School dropout registered in other family members	34840	-0.607	0.544	0.001	275.92	284.37
Low level of family income	34579	-0.754	0.451	0.001	275.18	285.66
Parents’ low level of education	34707.5	-0.685	0.493	0.001	275.54	285.03
Parents leaving to work abroad	35885	-0.026	0.98	0.000	279.13	278.77
Domestic violence	34163	-0.982	0.326	0.002	283.99	270.29

Table 2 shows the comparison between rural and urban schools at the family causes of school dropout. The results of the non-parametric Mann-Whitney U test confirm the existence of a statistically significant difference (U=29066.5, p=0) between schools in the urban environment (M=2.73, SD=1.486, Mdn=2) and those in the rural environment (M=2.24, SD=1.341, Mdn=2) on the cause of school dropout regarding the placement in alternative care. The magnitude of the effect of the independent variable is η²=0.026, and at least 2.7% of the variance of the variables can be explained by the area of school residence, being the largest effect obtained regarding the causes generated at the level of the student’s family situation.

According to the non-parametric test for the comparison of independent samples, the existence of a statistically significant difference (U=31150, p=0.005) between schools in the urban environment (M=2.11, SD=1.387, Mdn=2) and those in the rural environment (M=1.71, SD=1.008, Mdn=1) can be confirmed regarding the incarcerated parent case. The magnitude of the residence environment effect is η²=0.0144, thus at least 1.4% of the variance can be explained by the independent variable.

The hypothesis that there are significant differences between urban and rural schools regarding the prevalence of school dropout causes at the level of the student’s family situation was partially confirmed. Statistically significant differences were confirmed for two of the nine causes, i.e., placement in alternative care and incarcerated parents, with higher rates for urban schools, as shown in Figure 2.

Figure 2. Mean ranks for the causes of school dropout generated by the family situation

3.3 School Dimension

The respondents were presented with 11 dropout causes, generated by the school environment to which the student belongs, for evaluation. But statistically significant differences were not identified for eight of them as follows:

• Bullying (U=35930, p=1)

• Lack of empathy on the part of school staff towards students at risk of dropping out of school (U=35899, p=0.985)

• Lack of effective institutional strategies (U=34930, p=0.571)

• Lack of a motivational school climate (U=34883.5, p=0.549)

• Ethnic heterogeneity (U=32853, p=0.076)

• Limited resources at school level (U=34756.5, p=0.5)

• The transition from one level of education to another and the absence of programs to facilitate this transition (U=35783, p=0.934)

• Too restrictive disciplines (U=35602, p=0.847)

Table 3. Comparison between rural and urban schools regarding the school dropout causes generated by the school environment

	Mann-Whitney U	Z	Asymp. Sig. (2-Tailed)	η²	Mean Rank for the Urban	Mean Rank for the Rural
Bullying	35930	-0.001	1	0.000	279	279
Lack of support and guidance in choosing an appropriate educational and professional path	30218.5	-3.197	0.001	0.018	262.86	307.14
Lack of empathy from school staff towards students at risk of dropping out	35899	-0.019	0.985	0.000	279.09	278.84
Lack of effective institutional strategies	34930	-0.566	0.571	0.001	281.83	274.07
Lack of a motivational school climate	34883.5	-0.599	0.549	0.001	281.96	273.84
Too early start time for school activities	31708	-2.45	0.014	0.011	290.93	258.2
Ethnic heterogeneity	32853	-1.774	0.076	0.006	287.69	263.84
Limited resources at the school level	34756.5	-0.674	0.5	0.001	282.32	273.21
The transition from one level of education to another and the absence of programs to facilitate this transition	35783	-0.083	0.934	0.000	279.42	278.27
Discipline too restrictive	35602	-0.193	0.847	0.000	279.93	277.38
Number of students per class	31671.5	-2.458	0.014	0.011	291.03	258.02

Table 3 shows the results of the comparison between rural and urban schools regarding the school dropout causes generated by the school environment. The results of the Mann-Whitney U test confirm the existence of a statistically significant difference (U=30218.5, p=0.001) between schools in the urban environment (M=2.6, SD=1.489, Mdn=2) and those in the rural environment (M=3.07, SD =1.653, Mdn=3) on the lack of counseling and guidance in choosing an appropriate educational and professional path. The magnitude of the effect of the independent variable is η²=0.0183, and at least 1.8% of the variance can be explained by the area of school residence.

According to the non-parametric test for independent samples, significant differences (U=31708, p=0.014) between schools in the urban environment (M=2.22, SD=1.366, Mdn=2) and those in the rural environment (M=1.92, SD=1.176, Mdn=1) on the cause of morning start time of school activities can be observed, with a magnitude of the effect of the independent variable η²=0.011, explaining at least 1% of the variance at the level of this cause.

The existence of a statistically significant difference (U=31671.5, p=0.014) between schools in the urban environment (M=2.3, SD=1.42, Mdn=2) and those in the rural environment (M=2.01, SD=1.32, Mdn=2) regarding the size of student groups can be confirmed. The magnitude of the effect of the independent variable is η²=0.0108, with at least 1.1% of the variance of the responses being explained by the area of school residence.

The hypothesis that there are significant differences between urban and rural schools regarding the prevalence of the causes of school dropout in the sphere of the school to which the student belongs was partially confirmed. Statistically significant differences were observed (Figure 3) for three out of 11 causes, i.e., morning start time of school activities, the size of the student groups, and the lack of counseling and guidance in choosing an appropriate educational and professional path.

Figure 3. Mean ranks for the causes of school dropout generated by the school environment

3.4 Community Dimension

Considering the aspects of the community which might have a negative influence over students, six causes of school dropout are investigated, and significant differences between urban and rural schools were not identified for four of them as follows:

• The negative influence of social media platforms, the online environment and mass media (U=35381, p=0.76)

• The low level of education in the community (U=33292, p=0.142)

• COVID-19 pandemic (U=33201.5, p=0.128)

• High unemployment rate in the area where the student lives (U=33798.5, p=0.236)

Table 4. Comparison between rural and urban schools regarding the causes of school dropout generated by the community

	Mann-Whitney U	Z	Asymp. Sig. (2-Tailed)	η²	Mean Rank for the Urban	Mean Rank for the Rural
Belonging to an inappropriate social group	31649	-2.388	0.017	0.010	291.1	257.91
Lack of educational facilities in the proximity of students’ homes	30800.5	-2.909	0.004	0.015	293.49	253.73
Negative influence of social media platforms, the online environment and the media	35381	-0.306	0.76	0.000	280.55	276.29
Low level of education in the community	33292	-1.467	0.142	0.004	286.45	266
COVID-19 pandemic	33201.5	-1.521	0.128	0.004	286.71	265.55
High unemployment rate in the area where the student lives	33798.5	-1.184	0.236	0.003	272.98	289.5

Table 4 shows the results for the comparison between rural and urban schools regarding the causes of school dropout generated by the community. According to the results of the non-parametric test for independent samples, statistically significant differences (U=31649, p=0.017) between schools in the urban environment (M=3.96, SD=1.56, Mdn=4) and those in the rural environment (M=3.63, SD= 1.601, Mdn=4) regarding the belonging to an inappropriate social group can be confirmed. The magnitude of the effect of the independent variable is η²=0.0102, and at least 1% of the variance in rank can be explained by the area of school residence.

The Mann-Whitney U results attest to the existence of a statistically significant difference (U=30800.5, p=0.004) between schools in the urban environment (M=2.58, SD=1.517, Mdn=2) and those in the rural environment (M=2.19, SD= 1,341, Mdn=2) regarding the lack of educational facilities in the proximity of students’ homes. The magnitude of the effect of the independent variable is η²=0.0152, and at least 1.5% of the variance in rank can be explained by the area of school residence.

The hypothesis that there are significant differences between urban and rural schools regarding the prevalence of the causes of school dropout in the sphere of the community to which the student belongs was partially confirmed. Statistically significant differences were observed for two out of six causes, as noted in Figure 4, with a higher incidence in urban schools, i.e., lack of educational facilities in the proximity of students’ homes and belonging to an inappropriate social group.

Figure 4. Mean ranks for the causes of school dropout influenced by the community

4. Discussion

The results of this study shed light on the disparities between urban and rural schools in Romania regarding the causes of school dropout. The findings support the initial hypothesis that there are significant differences in the prevalence of dropout causes between urban and rural settings, albeit only partially. The analysis showed that while many causes were similarly perceived across both environments, a number of significant distinctions emerged in certain dimensions, with a generally higher incidence in urban schools.

The study confirmed significant differences in four out of the 14 individual-level causes examined: drug and alcohol use, juvenile delinquency, pregnancy, and health issues. These results suggest that urban schools have a higher incidence of these issues compared to rural schools. The finding that drug and alcohol use is more prevalent in urban schools aligns with previous research, which has often highlighted higher rates of substance abuse in urban areas due to greater accessibility and social pressures. Similarly, the higher rates of juvenile delinquency and pregnancy in urban areas can be linked to socio-economic factors and environmental influences commonly associated with urban settings, such as exposure to risky behaviors. While health issues were a more prominent concern in urban areas, this might be indicative of the strain that urban living places on students, including exposure to pollution and higher stress levels. Conversely, the lack of significant differences in other causes, such as lack of motivation, absenteeism, and early marriage, highlights that some risk factors for school dropout are universally distributed across both urban and rural settings.

In the family dimension, significant differences were found in two of the nine causes: placement in alternative care and having incarcerated parents, both of which were more frequently observed in urban schools. This suggests that urban students may face more family disruptions, likely linked to higher rates of poverty, crime, and unstable family structures in urban environments. The absence of significant differences in other family-related causes, such as low family income or domestic violence, points to the pervasive nature of these issues across both settings, suggesting that economic hardship and family struggles are common challenges irrespective of location.

Significant differences in the school dimension were observed for three out of 11 causes: the lack of counseling and guidance, early school start times, and large student group sizes. Urban schools reported higher levels of dissatisfaction regarding counseling and guidance services, which could reflect the greater diversity and complexity of student needs in urban areas. The issue of larger class sizes in urban schools is consistent with previous studies that highlight overcrowding as a challenge in urban education. Interestingly, rural schools expressed greater concern over early start times, possibly due to the logistical difficulties faced by students traveling longer distances to school.

In the community dimension, two out of six causes showed significant differences: belonging to an inappropriate social group and lack of educational facilities near the student’s home. Both issues were more prominent in urban schools. This finding is consistent with the idea that urban students are more likely to be exposed to a diverse range of social influences, including negative ones. The lack of nearby educational facilities, however, might seem counterintuitive in an urban setting, but it could be explained by the overcrowding of available resources in city areas, leading to an overall scarcity of accessible facilities.

5. Conclusions

This study partially confirms the hypothesis of the existence of significant differences between urban and rural environments regarding the prevalence of school dropout causes. Differences manifest significantly at the level of individual, family, school and community causes.

The study’s findings highlight the importance of tailoring dropout prevention strategies to the unique challenges faced by students in both urban and rural settings. In urban schools, addressing issues like substance abuse, delinquency, and family disruptions should be a priority, as should improving access to counseling and reducing class sizes. For rural schools, strategies should focus on improving the accessibility of school resources and addressing logistical challenges like early school start times. Moreover, these findings suggest that school dropout prevention programs need to adopt a more holistic approach that takes into account not only the individual and family factors but also the broader school and community contexts. Educational policymakers should consider increasing investments in counseling services and extracurricular programs in urban schools while also improving infrastructure and transportation services in rural areas to address early school start time and access to educational facilities.

In conclusion, the study contributes to the existing literature on school dropout by providing a nuanced comparison between urban and rural schools in Romania. While some causes of dropout, such as lack of motivation or low family income, appear to affect students across both environments equally, others like substance abuse and family disruption are more pronounced in urban areas. Future research should continue to explore the complex interplay of these factors and develop interventions that are sensitive to the unique needs of both urban and rural students.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability

The data used to support the research findings are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

Adelman, M., Haimovich, F., Ham, A., & Vazquez, E. (2018). Predicting school dropout with administrative data: New evidence from Guatemala and Honduras. Educ. Econ., 26(4), 356–372. [Google Scholar] [Crossref]

Agüero, J., Favara, M., Porter, C., & Sánchez, A. (2021). Do more school resources increase learning outcomes? Evidence from an extended school-day reform. IZA Inst. Lab. Econ., 14240. [Google Scholar] [Crossref]

Albright, K., Greenbaum, J., Edwards, S. A., & Tsai, C. (2020). Systematic review of facilitators of, barriers to, and recommendations for healthcare services for child survivors of human trafficking globally. Child Abuse Negl., 100, 104289. [Google Scholar] [Crossref]

Alifiyah, R. & Anshori, I. (2023). Legal protection for children in cases of domestic violence in the Indonesian households. El-Usrah: Jurnal Hukum Keluarga, 6(2), 348–361. [Google Scholar] [Crossref]

Bell, C. & Puckett, T. (2020). I want to learn but they won’t let me: Exploring the impact of school discipline on academic achievement. Urban Educ., 58(10), 2658–2688. [Google Scholar] [Crossref]

Bliss, F. (2024). School feeding as a core contribution to social security: Analyses and recommendations. AVE-Studien, no. 37b. [Google Scholar] [Crossref]

Bove, C. & Sharmahd, N. (2020). Beyond invisibility. Welcoming children and families with migrant and refugee background in ECEC settings. Eur. Early Childhood Educ. Res. J., 28(1), 1–9. [Google Scholar] [Crossref]

Cooc, N. (2023). National trends in special education and academic outcomes for English learners with disabilities. J. Spec. Educ., 57(2), 106–117. [Google Scholar] [Crossref]

De Witte, K., Nicaise, I., Lavrijsen, J., Van Landeghem, G., Lamote, C., & Van Damme, J. (2013). The impact of institutional context, education and labour market policies on early school leaving: A comparative analysis of EU countries. Eur. J. Educ., 48(3), 331–345. [Google Scholar]

Del Toro, J., Fine, A., & Wang, M. T. (2022). The intergenerational effects of paternal incarceration on children’s social and psychological well-being from early childhood to adolescence. Dev. Psychopathology, 35(2), 558–569. [Google Scholar] [Crossref]

Delap, E. (2009). Begging for Change: Research Findings and Recommendations on Forced Child Begging in Albania/Greece, India and Senegal. Anti-Slavery International, London, England. [Google Scholar]

Diaz-Serrano, L. & Stoyanova, A. P. (2023). The relationship between overweight and education revisited: A test of the selection hypothesis based on adolescents’ educational aspirations. Publ. Health, 225, 237–243. [Google Scholar] [Crossref]

Dimitropoulos, G., Lindenbach, D., Devoe, D. J., Gunn, E., Cullen, O., Bhattarai, A., Kuntz, J., Binford, W., Patten, S. B., & Arnold, P. D. (2022). Experiences of Canadian mental health providers in identifying and responding to online and in-person sexual abuse and exploitation of their child and adolescent clients. Child Abuse Negl., 124, 105448. [Google Scholar] [Crossref]

Dreier, H. & Luce, K. (2023). Alone and exploited, migrant children work brutal jobs across the US. New York Times. [Google Scholar]

Fan, W. & Wolters, C. A. (2012). School motivation and high school dropout: The mediating role of educational expectation. British J. Educational Psychology, 84(1), 22–39. [Google Scholar] [Crossref]

General Assembly Security Council. (2021). Annual report of the secretary-general on children and armed conflict. https://childrenandarmedconflict.un.org/document/annual-report-of-the-secretary-general-on-children-and-armed-conflict-2/ [Google Scholar]

Goldschmidt, K. (2020). The COVID-19 pandemic: Technology use to support the wellbeing of children. J. Pediatric Nursing, 53, 88–90. [Google Scholar] [Crossref]

Gordon, D. & Nandy, S. (2012). Measuring child poverty and deprivation. In Global Child Poverty and Well-Being (pp. 57–102). Bristol: Policy Press. [Google Scholar]

Guío, J. M., Choi, Á., & Escardibul Ferrá, J. O. (2016). Labor markets, academic performance and the risk of school dropout: Evidence for Spain. Social Sci. Res. Netw. [Google Scholar] [Crossref]

Haste, H. & Chopra, V. (2020). The futures of education for participation in 2050: Educating for managing uncertainty and ambiguity. UNESDOC, ED-2020/FoE-BP/22. [Google Scholar]

Institutul Național de Statistică. (2023). Baze de date statistice. [Google Scholar]

Kılıç, R. (2022). The problem of hunger in the world and a new model proposal to solve this problem. Balkan Sosyal Bilimler Dergisi, 11(21), 63–68. [Google Scholar] [Crossref]

Lestarini, R. (2023). Should I drop out of school? A study of the right to education for pregnant students. Yuridika, 38(3), 565–592. [Google Scholar] [Crossref]

Levkovich, I. & Elyoseph, Z. (2021). “I don’t know what to say”: Teachers’ perspectives on supporting bereaved students after the death of a parent. OMEGA J. Death Dying, 86(3), 945–965. [Google Scholar] [Crossref]

Marlow, S. A. & Rehman, N. (2021). The relationship between family processes and school absenteeism and dropout: a meta-analysis. Educational Dev. Psychologist, 38(1), 3–23. [Google Scholar] [Crossref]

Meyers, S., Rowell, K., Wells, M., & Smith, B. C. (2019). Teacher empathy: A model of empathy for teaching for student success. Coll. Teach., 67(3), 160–168. [Google Scholar] [Crossref]

Mihigo, I. M., Vermeylen, G., & Munguakonkwa, D. B. (2024). Child labour, school attendance and orphaned children in the Democratic Republic of the Congo. Discover Global Soc., 2(1), 8. [Google Scholar] [Crossref]

Ministerul Educației. (2023). Raport privind starea învățământului preuniversitar din România 2022. https://bpe.cpedu.ro/wp-content/uploads/listing-uploads/upload-pdf/2024/08/Raport-Starea-invatamantului-preuniversitar-2022-2023.pdf [Google Scholar]

Mireles-Rios, R., Rios, V. M., & Reyes, A. (2020). Pushed out for missing school: The role of social disparities and school truancy in dropping out. Educ. Sci., 10(4), 108. [Google Scholar] [Crossref]

Musa, A. Z. & Rais, H. (2023). Exploring the juvenile delinquency involvements of former young male juvenile delinquents. IIUM J. Educational Stud., 11(1), 119–133. [Google Scholar] [Crossref]

Muthami, K., Mwania, J. M., & Cheloti, S. K. (2023). Social media as a determinant of students’ dropout rates in secondary schools in Kenya. Br. J. Multi. Adv. Stud., 4(3), 1–15. [Google Scholar] [Crossref]

O’Donnell, A. W., Redmond, G., Gardner, A. A., Wang, J. J., & Mooney, A. (2023). Extracurricular activity participation, school belonging, and depressed mood: A test of the compensation hypothesis during adolescence. Appl. Dev. Sci., 28(4), 596–611. [Google Scholar] [Crossref]

Owens, J. A., Belon, K., & Moss, P. (2010). Impact of delaying school start time on adolescent sleep, mood, and behavior. Arch. Pediatrics Adolescent Med., 164(7), 608–614. [Google Scholar] [Crossref]

Papachristou, M. (2023). The school dropout of Roma students: A research effort on the causes of the phenomenon. Eur. J. Educ. Stud., 10(10). [Google Scholar] [Crossref]

Paraman, M. & Hussain, R. B. M. (2022). Peer’s pressure effects: Secondary school student’s dropout behaviour and young offenders. E-BANGI J., 19(2), 142–159. [Google Scholar] [Crossref]

Paulsrud, D. & Nilholm, C. (2020). Teaching for inclusion–A review of research on the cooperation between regular teachers and special educators in the work with students in need of special support. Int. J. Inclusive Educ., 27(4), 541–555. [Google Scholar] [Crossref]

Pedditzi, M. L. (2024). School satisfaction and self-efficacy in adolescents and intention to drop out of school. Int. J. Environ. Res. Publ. Health, 21(1), 111. [Google Scholar] [Crossref]

Petrowski, N., Cappa, C., & Gross, P. (2017). Estimating the number of children in formal alternative care: Challenges and results. Child Abuse Negl., 70, 388–398. [Google Scholar] [Crossref]

Rafferty, Y. (2016). Challenges to the rapid identification of children who have been trafficked for commercial sexual exploitation. Child Abuse Negl., 52, 158–168. [Google Scholar]

Rahman, M. A., Renzaho, A. M., Kundu, S., Awal, M. A., Ashikuzzaman, M., Fan, L., Ahinkorah, B. O., Okyere, J., Kamara, J. K., & Mahumud, R. A. (2023). Prevalence and factors associated with chronic school absenteeism among 207,107 in-school adolescents: Findings from cross-sectional studies in 71 low-middle and high-income countries. PLoS One, 18(5), e0283046. [Google Scholar] [Crossref]

Ratusniak, C. & Silva, C. C. D. (2023). School dropout or expulsion: Why do student-mothers leave school? Psicologia Escolar e Educacional, 27, e243705. [Google Scholar] [Crossref]

Sabates, R., Westbrook, J., Akyeampong, K., & Hunt, F. (2010). School Drop Out: Patterns, Causes, Changes and Policies. Paris: United Nations Educational, Scientific and Cultural Organisation (UNESCO). [Google Scholar]

Silverstein, M. & Zuo, D. (2021). Grandparents caring for grandchildren in rural China: Consequences for emotional and cognitive health in later life. Aging & Mental Health, 25(11), 2042–2052. [Google Scholar] [Crossref]

Stoddard, S. A., Hughesdon, K., Khan, A., & Zimmerman, M. A. (2020). Feasibility and acceptability of a future‐oriented empowerment program to prevent substance use and school dropout among school‐disengaged youth. Publ. Health Nursing, 37(2), 251–261. [Google Scholar] [Crossref]

Ünlü, M. & Avci, R. (2023). Examination of aggression and school attitudes of high school students exposed to teacher violence and peer bullying. J. Sch. Violence, 22(4), 474–489. [Google Scholar] [Crossref]

Vadivel, B., Alam, S., Nikpoo, I., & Ajanil, B. (2023). The impact of low socioeconomic background on a child’s educational achievements. Educ. Res. Int., 2023(1), 6565088. [Google Scholar] [Crossref]

Van der Berg, S., Van Wyk, C., & Selkirk, R. (2020). Schools in the Time of COVID-19: Possible Implications for Enrolment, Repetition and Dropout. Stellenbosch: University of Stellenbosch. [Google Scholar]

Yusof, R., Harith, N. H. M., Lokman, A., Abd Batau, M. F., Zain, Z. M., & Rahmat, N. H. (2023). A study of perception on students’ motivation, burnout and reasons for dropout. Int. J. Academic Res. Bus. Social Sci., 13(7), 403–432. [Google Scholar] [Crossref]

Zeragaber, T. Y., Teame, G. T., & Tsighe, Z. (2024). Assessing the effect of home-to-school distance on student dropout rate in Adi-Keyih sub-zone, Eritrea. Int. J. Educational Res. Open, 7, 100340. [Google Scholar]

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Miron, M. & Mistrean, L. (2024). A Comparative Analysis of School Dropout Causes in Rural and Urban Romania. Educ. Sci. Manag., 2(3), 134-144. https://doi.org/10.56578/esm020302

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